Theory and Modeling of Platinum Surface Reactions
Bibliographic record
Abstract
The formation and reduction of surface oxide species determine both the electrocatalytic activity of Pt towards the oxygen reduction reaction as well as the rate of corrosive Pt dissolution [1]. We perform theory and modeling work to rationalize the various stages of oxide formation and reduction at Pt. Our mechanistic models establish relations between metal phase potential and surface oxidation state that govern the transient current response of the electrode, as probed for instance in cyclic voltammetry. In the first part, we will discuss a recently developed kinetic model for oxide formation and reduction at Pt in the voltage range of 0.65–1.15 V [2]. The model is evaluated against electrochemical [3], spectroscopic [4] and computational studies [5]. In the second part, we will present a kinetic model of oxide growth on platinum in the high voltage regime, above 1.15 V. The governing equations of the oxide growth model account for mass and charge conservation, species migration, and electric field effects. As the outcome, we will obtain a generalized oxide growth law of platinum. The results will be compared to experimental cyclic voltammetry data to extract rates of kinetic and transport processes. Moreover, the model incorporates a mechanism of platinum dissolution. Using this model, we strive to explain the dramatically enhanced rate of Pt dissolution at extended surfaces and in nanoparticle systems, observed in experimental studies that involved voltage cycling through the high voltage regime [6,7,8]. Knowledge of the mechanisms of growth and reduction of oxides on platinum will allow us to refine our theory of platinum dissolution in polymer electrolyte fuel cells [9]. References [1] A. Seyeux, V. Maurice, and P. Marcus, J. Electrochem. Soc. 160, C189 (2013). [2] S. G. Rinaldo, W. Lee, J. Stumper, and M. Eikerling, Electrocatalysis 5, 262 (2014). [3] A.M. Gómez-Marín, J. Clavilier, J.M. Feliu, J. Electroanal. Chem. 688, 360 (2013) [4] M. Wakisaka, H. Suzuki, S. Mitsui, H. Uchida, M. Watanabe, Langmuir 25, 1897 (2009) [5] L. Wang, A. Roudgar, M. Eikerling, J. Phys. Chem. C 113, 17989 (2009) [6] S. G. Rinaldo, P. Urchaga, J. Hu, W. Lee, J. Stumper, C. Rice, and M. Eikerling, Phys. Chem. Chem. Phys., submitted. [7] A. A. Topalov, S. Cherevko, A. R. Zeradjanin, J. C. Meier, I. Katsounaros, and K. J. J. Mayrhofer, Chemical Science 5, 631 (2014). [8] L. Xing, M. A. Hossain, M. Tian, D. Beauchemin, K. T. Adjemian, and G. Jerkiewicz, Electrocatalysis 5, 96 (2014). [9] S. G. Rinaldo, W. Lee, J. Stumper, and M. Eikerling, Physical Review E 86, 041601 (2012).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".